The empirical mode decomposition (EMD) has been developed by N.E. Huang et al. in 1998 as an iterative method to decompose a nonlinear and nonstationary univariate function additively into multiscale components. These components called intrinsic mode functions (IMFs) are constructed such that they are approximately orthogonal to each other with respect to the L2 inner product. Moreover, the com...

There are some methods to decompose a signal into different components such as: Fourier decomposition and wavelet decomposition. But they have limitations in some aspects. Recently, there is a new signal decomposition algorithm called the Empirical Mode Decomposition (EMD) Algorithm which provides a powerful tool for adaptive multiscale analysis of nonstationary signals. Recent works have demon...

Background The chess game is a good example of cognitive task which needs a lot of training and experience. The aim of this work is to compare applicability of two nonlinear methods Higuchi Fractal Dimension and Empirical Mode Decomposition in analysis of EEG data recorded during chess match. We analyzed data of three master chess players registered during their matches with computer program. M...

Huang’s data-driven technique of Empirical Mode Decomposition (EMD) is presented, and issues related to its effective implementation are discussed. A number of algorithmic variations, including new stopping criteria and an on-line version of the algorithm, are proposed. Numerical simulations are used for empirically assessing performance elements related to tone identification and separation. T...

Article history: Iris recognition is known Received 5 December 2006 Received in revised form 31 July 2008 Accepted 28 September 2008 Available online 26 October 2008

Gait recognition is identifying human beings by the manner in which they walk. It has been shown by several researchers that human beings have the ability to recognize other people by their gait. Machine recognition of gait is becoming increasingly important for surveillance, awarespaces etc. A number of methods have been proposed by different researchers in the recent past for this purpose. Mo...

The empirical mode decomposition (EMD) was a method pioneered by Huang et al [8] as an alternative technique to the traditional Fourier and wavelet techniques for studying signals. It decomposes a signal into several components called intrinsic mode functions (IMF), which have shown to admit better behaved instantaneous frequencies via Hilbert transforms. In this paper we propose an alternative...

This paper discusses some mat hematical issues related to empirical mode decomposition (EMD). A B-spline EMD algorithm is introduced and developed for the convenience of mathematical studies. The numerical analysis using both simulated and practical signals and application examples from vibration analysis indicate that the B-spline algorithm has a comparable performance to that of the original ...

Spatially varying intensity noise is a common source of distortion in images. Bias field noise is one example of such distortion that is often present in the magnetic resonance (MR) images. In this paper, we first show that empirical mode decomposition (EMD) can considerably reduce the bias field noise in the MR images. Then, we propose two hierarchical multi-resolution EMD-based algorithms for...